Function Approximation and Nonlinear Regression

Create a neural network to generalize nonlinear relationships between example inputs and outputs

Apps

Solve fitting problem using two-layer feed-forward networks

`nftool`	Open Neural Net Fitting app
`view`	View shallow neural network

`fitnet`	Function fitting neural network
`feedforwardnet`	Generate feedforward neural network
`cascadeforwardnet`	Generate cascade-forward neural network

`train`	Train shallow neural network
`trainlm`	Levenberg-Marquardt backpropagation
`trainbr`	Bayesian regularization backpropagation
`trainscg`	Scaled conjugate gradient backpropagation
`trainrp`	Resilient backpropagation
`mse`	Mean squared normalized error performance function
`regression`	(Not recommended) Perform linear regression of shallow network outputs on targets
`ploterrhist`	Plot error histogram
`plotfit`	Plot function fit
`plotperform`	Plot network performance
`plotregression`	Plot linear regression
`plottrainstate`	Plot training state values
`genFunction`	Generate MATLAB function for simulating shallow neural network

Shallow Networks for Pattern Recognition, Clustering and Time Series
Use apps and functions to design shallow neural networks for function fitting, pattern recognition, clustering, and time series analysis.
Fit Data with a Shallow Neural Network
Train a shallow neural network to fit a data set.
Create, Configure, and Initialize Multilayer Shallow Neural Networks
Prepare a multilayer shallow neural network.
Body Fat Estimation
This example illustrates how a function fitting neural network can estimate body fat percentage based on anatomical measurements.
Train and Apply Multilayer Shallow Neural Networks
Train and use a multilayer shallow network for function approximation or pattern recognition.
Analyze Shallow Neural Network Performance After Training
Analyze network performance and adjust training process, network architecture, or data.
Deploy Shallow Neural Network Functions
Simulate and deploy trained shallow neural networks using MATLAB^® tools.
Deploy Training of Shallow Neural Networks
Learn how to deploy training of shallow neural networks.

Shallow Neural Networks with Parallel and GPU Computing
Use parallel and distributed computing to speed up neural network training and simulation and handle large data.
Automatically Save Checkpoints During Neural Network Training
Save intermediate results to protect the value of long training runs.
Optimize Neural Network Training Speed and Memory
Make neural network training more efficient.
Train Shallow Networks on CPUs and GPUs
Speed up training and simulation of large problems using shallow networks.

Choose Neural Network Input-Output Processing Functions
Preprocess inputs and targets for more efficient training.
Configure Shallow Neural Network Inputs and Outputs
Learn how to manually configure the network before training using the configure function.
Divide Data for Optimal Neural Network Training
Use functions to divide the data into training, validation, and test sets.
Choose a Multilayer Neural Network Training Function
Comparison of training algorithms on different problem types.
Improve Shallow Neural Network Generalization and Avoid Overfitting
Learn methods to improve generalization and prevent overfitting.
Train Neural Networks with Error Weights
Learn how to use error weighting when training neural networks.
Normalize Errors of Multiple Outputs
Learn how to fit output elements with different ranges of values.

Workflow for Neural Network Design
Learn the primary steps in a neural network design process.
Four Levels of Neural Network Design
Learn the different levels of using neural network functionality.
Multilayer Shallow Neural Networks and Backpropagation Training
Workflow for designing a multilayer shallow feedforward neural network for function fitting and pattern recognition.
Multilayer Shallow Neural Network Architecture
Learn the architecture of a multilayer shallow neural network.
Understanding Shallow Network Data Structures
Learn how the format of input data structures affects the simulation of networks.
Sample Data Sets for Shallow Neural Networks
List of sample data sets to use when experimenting with shallow neural networks.
Neural Network Object Properties
Learn properties that define the basic features of a network.
Neural Network Subobject Properties
Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.